406 citations · 710 across the 30 of their papers we have counts for
37 papers
Improved Instruction Ordering in Recipe-Grounded Conversation
Duong Minh Le, Ruohao Guo, Wei Xu +1
In this paper, we study the task of instructional dialogue and focus on the cooking domain. Analyzing the generated output of the GPT-J model, we reveal that the primary challenge…
Meta-Tuning LLMs to Leverage Lexical Knowledge for Generalizable Language Style Understanding
Ruohao Guo, Wei Xu, Alan Ritter
Language style is often used by writers to convey their intentions, identities, and mastery of language. In this paper, we show that current large language models struggle to captu…
Are Large Language Models Robust Coreference Resolvers?
Nghia T. Le, Alan Ritter
Recent work on extending coreference resolution across domains and languages relies on annotated data in both the target domain and language. At the same time, pre-trained large la…
Schema-Driven Information Extraction from Heterogeneous Tables
Fan Bai, Junmo Kang, Gabriel Stanovsky +3
In this paper, we explore the question of whether large language models can support cost-efficient information extraction from tables. We introduce schema-driven information extrac…
Translation and Fusion Improves Zero-shot Cross-lingual Information Extraction
Yang Chen, Vedaant Shah, Alan Ritter
Large language models (LLMs) combined with instruction tuning have shown significant progress in information extraction (IE) tasks, exhibiting strong generalization capabilities to…
Distill or Annotate? Cost-Efficient Fine-Tuning of Compact Models
Junmo Kang, Wei Xu, Alan Ritter
Fine-tuning large models is highly effective, however, inference can be expensive and produces carbon emissions. Knowledge distillation has been shown to be a practical solution to…
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?
Yang Chen, Hexiang Hu, Yi Luan +4
Pre-trained vision and language models have demonstrated state-of-the-art capabilities over existing tasks involving images and texts, including visual question answering. However,…
Do CoNLL-2003 Named Entity Taggers Still Work Well in 2023?
Shuheng Liu, Alan Ritter
The CoNLL-2003 English named entity recognition (NER) dataset has been widely used to train and evaluate NER models for almost 20 years. However, it is unclear how well models that…
Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments
Ethan Mendes, Yang Chen, Wei Xu +1
We present a human-in-the-loop evaluation framework for fact-checking novel misinformation claims and identifying social media messages that support them. Our approach extracts che…
Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts
Nghia T. Le, Fan Bai, Alan Ritter
Anaphora resolution is an important task for information extraction across a range of languages, text genres, and domains, motivating the need for methods that do not require large…
Frustratingly Easy Label Projection for Cross-lingual Transfer
Yang Chen, Chao Jiang, Alan Ritter +1
Translating training data into many languages has emerged as a practical solution for improving cross-lingual transfer. For tasks that involve span-level annotations, such as infor…
Stanceosaurus: Classifying Stance Towards Multilingual Misinformation
Jonathan Zheng, Ashutosh Baheti, Tarek Naous +2
We present Stanceosaurus, a new corpus of 28,033 tweets in English, Hindi, and Arabic annotated with stance towards 251 misinformation claims. As far as we are aware, it is the lar…
SynKB: Semantic Search for Synthetic Procedures
Fan Bai, Alan Ritter, Peter Madrid +2
In this paper we present SynKB, an open-source, automatically extracted knowledge base of chemical synthesis protocols. Similar to proprietary chemistry databases such as Reaxsys,…
Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts
Ashutosh Baheti, Maarten Sap, Alan Ritter +1
Dialogue models trained on human conversations inadvertently learn to generate toxic responses. In addition to producing explicitly offensive utterances, these models can also impl…
Pre-train or Annotate? Domain Adaptation with a Constrained Budget
Fan Bai, Alan Ritter, Wei Xu
Recent work has demonstrated that pre-training in-domain language models can boost performance when adapting to a new domain. However, the costs associated with pre-training raise…
Process-Level Representation of Scientific Protocols with Interactive Annotation
Ronen Tamari, Fan Bai, Alan Ritter +1
We develop Process Execution Graphs (PEG), a document-level representation of real-world wet lab biochemistry protocols, addressing challenges such as cross-sentence relations, lon…
WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab Protocols
Jeniya Tabassum, Sydney Lee, Wei Xu +1
This paper presents the results of the wet lab information extraction task at WNUT 2020. This task consisted of two sub tasks: (1) a Named Entity Recognition (NER) task with 13 par…
Model Selection for Cross-Lingual Transfer
Yang Chen, Alan Ritter
Transformers that are pre-trained on multilingual corpora, such as, mBERT and XLM-RoBERTa, have achieved impressive cross-lingual transfer capabilities. In the zero-shot transfer s…
Measuring Forecasting Skill from Text
Shi Zong, Alan Ritter, Eduard Hovy
People vary in their ability to make accurate predictions about the future. Prior studies have shown that some individuals can predict the outcome of future events with consistentl…
Extracting a Knowledge Base of COVID-19 Events from Social Media
Shi Zong, Ashutosh Baheti, Wei Xu +1
In this paper, we present a manually annotated corpus of 10,000 tweets containing public reports of five COVID-19 events, including positive and negative tests, deaths, denied acce…
Fluent Response Generation for Conversational Question Answering
Ashutosh Baheti, Alan Ritter, Kevin Small
Question answering (QA) is an important aspect of open-domain conversational agents, garnering specific research focus in the conversational QA (ConvQA) subtask. One notable limita…
Code and Named Entity Recognition in StackOverflow
Jeniya Tabassum, Mounica Maddela, Wei Xu +1
There is an increasing interest in studying natural language and computer code together, as large corpora of programming texts become readily available on the Internet. For example…
An Empirical Study of Pre-trained Transformers for Arabic Information Extraction
Wuwei Lan, Yang Chen, Wei Xu +1
Multilingual pre-trained Transformers, such as mBERT (Devlin et al., 2019) and XLM-RoBERTa (Conneau et al., 2020a), have been shown to enable the effective cross-lingual zero-shot…
SemEval-2013 Task 2: Sentiment Analysis in Twitter
Preslav Nakov, Zornitsa Kozareva, Alan Ritter +3
In recent years, sentiment analysis in social media has attracted a lot of research interest and has been used for a number of applications. Unfortunately, research has been hinder…